What is an AI readiness assessment?

An AI readiness assessment is a structured evaluation of whether an organisation is positioned to succeed with AI, across dimensions such as strategy, data, talent, platform, governance, and operating model. It identifies the constraints to address before investing.

What should an AI readiness checklist cover?

Strategy and sponsorship, data readiness, talent and skills, platform and tooling, governance and risk, and the operating model for running systems after launch. A weakness in any single dimension can stall an initiative.

How long does an AI readiness assessment take?

Typically a few weeks, depending on the size of the organisation and access to data and stakeholders. The output is an honest read of readiness plus a prioritised roadmap of what to address first.

AI Readiness Assessment: A Checklist for Enterprises

Before investing in AI, it is worth an honest read of whether your organisation is ready to succeed with it. This checklist covers the six dimensions that most determine the outcome.

6 min read·By Chrysilla Rodrigues

Six dimensions of AI readiness

AI readiness is not a single score — it is a balance across several dimensions. A weakness in any one of them tends to stall otherwise-promising initiatives.

The six dimensions of enterprise AI readiness.

The checklist

Strategy and sponsorship

●Is there a clear business problem AI is meant to solve?
●Is there senior sponsorship and a budget owner?
●Are success metrics defined up front?

Data readiness

●Is the relevant data accessible, reasonably clean, and permitted for use?
●Do you know where the data lives and who owns it?

Talent and skills

●Do you have, or can you access, the skills to build and operate AI systems?
●Is there someone accountable for the system after launch?

Platform and tooling

●Can AI systems integrate with your existing tools and infrastructure?
●Is there a path to deploy and monitor them?

Governance and risk

●Are data, privacy, and compliance constraints understood?
●Is there a process for reviewing AI decisions and handling errors?

Operating model

●Who will run the system day to day once it is live?
●Is there a plan for evaluation, monitoring, and improvement over time?

Turning the checklist into a plan

Score each dimension honestly. The lowest one is usually where to focus first — it is the constraint holding the rest back. From there, a prioritised roadmap turns gaps into sequenced, fundable work.